diff --git a/ppdiffusers/ppdiffusers/models/attention_processor.py b/ppdiffusers/ppdiffusers/models/attention_processor.py
index 8894b1447..003bf22a4 100644
--- a/ppdiffusers/ppdiffusers/models/attention_processor.py
+++ b/ppdiffusers/ppdiffusers/models/attention_processor.py
@@ -20,7 +20,7 @@
 from paddle import einsum, nn
 
 from ..utils import USE_PEFT_BACKEND, deprecate, logging
-from ..utils.import_utils import is_ppxformers_available
+from ..utils.import_utils import is_npu_available, is_ppxformers_available
 from ..utils.paddle_utils import maybe_allow_in_graph
 from .lora import LoRACompatibleLinear, LoRALinearLayer
 
@@ -111,7 +111,7 @@ def __init__(
         super().__init__()
 
         # To prevent circular import.
-        from .normalization import  RMSNorm, FP32LayerNorm, LpNorm
+        from .normalization import FP32LayerNorm, LpNorm, RMSNorm
 
         self.inner_dim = dim_head * heads
         self.inner_dim = out_dim if out_dim is not None else dim_head * heads
@@ -251,7 +251,7 @@ def __init__(
         # We use the AttnProcessor2_5 by default when paddle 2.5 is used which uses
         # paddle.nn.functional.scaled_dot_product_attention_ for native Flash/memory_efficient_attention
         if processor is None:
-            processor = AttnProcessor2_5() if is_ppxformers_available() else AttnProcessor()
+            processor = AttnProcessor2_5() if is_ppxformers_available() or is_npu_available() else AttnProcessor()
         self.set_processor(processor)
 
     @property
@@ -997,12 +997,20 @@ def __call__(
             encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
             encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
 
-            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.reshape([batch_size, -1, attn.heads, head_dim])
-            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.reshape([batch_size, -1, attn.heads, head_dim])
-            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.reshape([batch_size, -1, attn.heads, head_dim])
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.reshape(
+                [batch_size, -1, attn.heads, head_dim]
+            )
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.reshape(
+                [batch_size, -1, attn.heads, head_dim]
+            )
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.reshape(
+                [batch_size, -1, attn.heads, head_dim]
+            )
 
             if attn.norm_added_q is not None:
-                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj, begin_norm_axis=3)
+                encoder_hidden_states_query_proj = attn.norm_added_q(
+                    encoder_hidden_states_query_proj, begin_norm_axis=3
+                )
             if attn.norm_added_k is not None:
                 encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj, begin_norm_axis=3)
 
diff --git a/ppdiffusers/ppdiffusers/patches/paddle_patch.py b/ppdiffusers/ppdiffusers/patches/paddle_patch.py
index b5a507f93..83634ad30 100644
--- a/ppdiffusers/ppdiffusers/patches/paddle_patch.py
+++ b/ppdiffusers/ppdiffusers/patches/paddle_patch.py
@@ -351,27 +351,30 @@ def to(self=None, device=None, dtype=None, blocking=None):
 
 nn.Layer.to = to
 
-from ..utils.import_utils import is_ppxformers_available, is_npu_available
+from ..utils.import_utils import is_npu_available, is_ppxformers_available
 
 if is_npu_available():
     for lib in os.listdir(os.getenv("CUSTOM_DEVICE_ROOT")):
         if lib.endswith(".so"):
-            paddle.utils.cpp_extension.extension_utils.load_op_meta_info_and_register_op(
-                lib
-            )
+            paddle.utils.cpp_extension.extension_utils.load_op_meta_info_and_register_op(lib)
     from paddle.base import core
-    def scaled_dot_product_attention_npu(query,
-                                         key,
-                                         value,
-                                         attn_mask=None,
-                                         dropout_p=0.0,
-                                         is_causal=False,
-                                         training=True,
-                                         name=None,
-                                         fixed_seed_offset=None,
-                                         return_softmax=False,
-                                         is_triangle_upper_mask=True,
-                                         ):
+
+    def scaled_dot_product_attention_npu(
+        query,
+        key,
+        value,
+        attn_mask=None,
+        actual_seq_q_len=None,
+        actual_seq_kv_len=None,
+        dropout_p=0.0,
+        is_causal=False,
+        training=True,
+        name=None,
+        fixed_seed_offset=None,
+        return_softmax=False,
+        is_triangle_upper_mask=True,
+        is_varlen=False,
+    ):
         out = core.eager._run_custom_op(
             "flash_attention_npu",
             query,
@@ -379,13 +382,17 @@ def scaled_dot_product_attention_npu(query,
             value,
             fixed_seed_offset,
             attn_mask,
+            actual_seq_q_len,
+            actual_seq_kv_len,
             dropout_p,
             is_causal,
             return_softmax,
             not training,
             is_triangle_upper_mask,
+            is_varlen,
         )[0]
         return out
+
     paddle.nn.functional.scaled_dot_product_attention_npu = scaled_dot_product_attention_npu
 
 if is_ppxformers_available() or is_npu_available():
@@ -407,8 +414,9 @@ def scaled_dot_product_attention_npu(query,
             paddle.ones((1, 1, 2, 40), dtype=paddle.float16),
             attn_mask=paddle.ones((1, 2, 1, 1), dtype=paddle.float16),
         )
-        
+
         from paddle.nn.functional.flash_attention import flash_attention
+
         _ = flash_attention(
             paddle.ones((1, 1, 2, 40), dtype=paddle.float16),
             paddle.ones((1, 1, 2, 40), dtype=paddle.float16),